Geographical data storage assignment based on ontological relevancy

ABSTRACT

System and method for distributing channelized content to a plurality of cohesive local networks. The channelized content is aggregated at a remote central processor with associated storage according to ontological relevancy, and then distributed over a network such as the internet to the local networks. The consumers connected to the respective local networks receive that channelized data with low latency and improved data rates.

RELATED APPLICATIONS

This application is related to, and claims the benefit of, U.S. PatentApplications Ser. No. 61/770,163, filed Feb. 27, 2013; Ser. No.61/770,186; Ser. No. 61/770,204; Ser. No. 61/770,211; all filed on Feb.27,2013 and incorporated herein by reference as though set forth infull.

FIELD OF THE INVENTION

The present invention relates generally to the field of internet contentdelivery systems and methods, and more particularly relates methods,systems and techniques for automatically selecting and caching contentat edge servers to provide significantly faster content delivery to, forexample, multi-dwelling units.

BACKGROUND OF THE INVENTION

Fast delivery of internet content has long been desired by users of theinternet. However, bandwidth limitations have historically existed whichseriously limited the ability of the internet infrastructure to meet theever-increasing demands of users for more content, delivered morequickly.

The limitations of the existing internet infrastructure are nowhere moreapparent than the exploding demand for fast downloading of video contentfrom the internet. However, video requires substantially greaterbandwidth than most other content and, as a result, such consumer demandhas placed substantial strain on the internet infrastructure.

While services such as Akamai have attempted to place limited,pre-designated content at the edge of the internet, such efforts aretypically limited to icons, images and ads, and do not include theactual content that users desire to see.

As a result, there has long been a need for a system which caneffectively “speed up” delivery of desired content without substantialdelay caused by the inherent bandwidth limitations associated with mostinternet feeds.

SUMMARY OF THE INVENTION

The present invention provides an efficient system and method foridentifying, caching, and delivering at high speed, the content thatusers in, for example, a multiple-dwelling unit are likely to want todownload based on prior usage characteristics augmented by trend datathe system aggregates across a plurality of similar and dissimilarmultiple-dwelling units and the users within.

In an aspect of the invention, the system comprises local contentstorage and an associated local network appliance deployed proximate to,or within, a multi-dwelling unit such as an office building, anapartment building, a dormitory, or other business or residentialstructure. Although, for convenience of illustration, the followingdescription assumes a multi-dwelling unit in many cases, in someembodiments the local appliance can serve either groups of consumersdistributed geographically, or only a single consumer. The local networkappliance communicates with consumer devices, and also communicates overthe internet with original content servers and, importantly in someembodiments, a central processing cloud.

The central processing cloud cooperates with the local network applianceto identify content that consumers desire to download. The centralprocessing cloud includes structures and methods for identifying contentlikely to be downloaded by the consumer devices, and communicates overthe internet with the server where the desired content is originallymaintained. The metadata associated with the original content iscommunicated to the central processing cloud, and a copy of the originalcontent along with its associated metadata are downloaded and stored.The metadata is used by a processing cluster, using a variety offiltering and evaluation criteria, to develop ontological correlations,or relevancies, among the content. In general, the filtering andevaluation criteria use predictive algorithms and seek to identifycontent that is likely to be desired for download by the consumerslocated at, for example, a particular multi-dwelling unit. The content,once so correlated, is then grouped or aggregated into “channels”. In anembodiment, the content comprising at least one channel is thendownloaded to the local network appliance and stored on the localcontent storage associated with the particular dwelling unit.

A high speed local network connects the local network appliance and itsassociated local content storage, where the channel of content isstored, to the consumer devices. When a consumer at that multi-dwellingunit seeks to download data that is included within the channel, thedata is essentially immediately available, without any of the delays andlatency associated with conventional internet downloads, and thus theconsumer receives the desired content at extremely fast data rates.

The central processing cloud includes functionality for identifying theorigin of requests for content, using data received from the localnetwork appliance. In at least some embodiments, personal dataassociated with such requests is “blurred out” or anonymized at thelocal appliance level, to minimize the risk of personal data beingcompromised. However, in at least some embodiments, it is possibleeither to use the data without such blurring, or to blur the data at theremote, central processing cloud. The central processing cloud alsoprovides functionality for complying with releases of content that aretime-stamped.

Geographical distribution, and updating, of the channels to theirrespective cohesive local networks, where the channels are based onontological relevancy, is accomplished by the network links between thecentral processing cloud and the local network appliance. In someinstances, load on the local cohesive network is sufficiently large thatmore than one local network appliance and associated content store isappropriate. In such cases, a regional appliance and associated contentstore can be used in some embodiments, and a content delivery node mayalso be used in some embodiments.

It is therefore one object of the present invention to provide a localcontent server with associated storage for delivering requested contentat accelerated data rates, substantially higher than conventionalinternet service providers.

It is a further object of the present invention to evaluate requests forinternet content originating from a location, and, based on suchrequests, develop groups of content that anticipate future requests fromthat location.

These and other objects of the present invention will be apparent fromthe following detailed description, taken together with the appendedFigures.

THE FIGURES

FIG. 1 shows a basic arrangement of system for practicing an embodimentof one method in accordance with the present invention.

FIG. 2 illustrates a tiered flow of internet content in accordance withthe present invention.

FIG. 3 illustrates a web proxy process in accordance with the presentinvention.

FIGS. 4A-4B illustrate the data mining process of the present invention.

FIG. 5 illustrates the central data process by which content isaggregated into channels.

FIG. 6 illustrates a more robust version of FIG. 1, in which a pluralityof local network appliances form a tiered network to provide channelizedcontent to a plurality of associated multiple dwelling units or otherconsumers.

FIG. 7 illustrates the “life cycle” of content channelized for use bythe present system.

DETAILED DESCRIPTION OF THE INVENTION

Referring first to FIG. 1, a group of one or more consumer devices 100,a local network appliance 105, and an associated local content storage110 form a small, geographically cohesive edge network 115. In at leastsome embodiments, the consumer devices can be any form ofnetwork-connected device capable of requesting network content, such aslaptop computers, desktop computers, smartphones, game consoles, etc.The local network appliance 105 can be, in at least some embodiments, acomputer server or other device capable of redirecting requests to a webproxy and controlling communications with the internet cloud 120, andalso capable of managing data flow with the local content storage 110.

In a typical arrangement, the local network appliance 105 receivesrequests for content from the consumer devices 100, with the two beinglinked by a high bandwidth local area network connection. If the contentresides on the local content storage 110, the content is served fromthere back to the consumer device 100 making the request. It will beappreciated that, if the requested content is already stored on thelocal content storage device 110, then the content is served to theconsumer device with essentially no latency and at the maximum data ratepermitted by the local area network connection. That data rate istypically much faster than the internet infrastructure, and thus theuser experience for the consumer is that the requested content arrivesessentially instantaneously.

It will be appreciated, then, that an objective of some aspects of thepresent invention is to ensure that, as often as possible, the contentthat any consumer 100 requests already resides on local content storage110. For at least some embodiments, the remainder of the system of thepresent invention is designed to achieve such a result. For purposes ofthe present discussion, the content stored on the local storage 110 willbe considered to be grouped or aggregated into one or more “channels”,with the composition of each “channel” developed as the result of thepast usage characteristics of all users operating consumer devices 100connected to any geographically connected edge network 115 associatedwith the same central processing cloud 125.

For some embodiments of the invention, in the event that the requestedcontent is not on the local content storage, the appliance 105communicates over the internet cloud 120 with a central processing cloud125, which forms part of the system of the present invention in at leastsome embodiments. For those embodiments in which the central processingcloud 125 exists, the central processing cloud comprises a metadatareceiver 130, a metadata processing cluster 135, central content storage140, metadata storage 145, and a metadata/content publisher 150. Themetadata receiver 130, processing cluster 135 and publisher 150 can allbe regarded as software functionalities operating on one or moreservers, which functions are explained in greater detail hereinafter.

In general, the metadata receiver receives consumer requests for contentfrom appliance 105, and forwards those requests to processing cluster135. Among other functions, the processing cluster determines whetherthe requested content is already stored on central content store 140. Ifthe requested content was not on local content storage 110, but is oncentral content store 140, the content may, in some embodiments, beserved back to the local network appliance 105 via publisher 150 andinternet cloud 120. In other embodiments, content that is not on thelocal content storage is retrieved from the original content server.

The download of stored content from the remote central processing cloudcan be particularly desirable in multiple instances. For example, wherethe internet infrastructure connection between the central processingcloud and the local network appliance is carrier grade, such that it hasbandwidth in the range of 10,000 Mbps, the high bandwidth can beutilized to make spot deliveries of content to local network appliances.In addition, direct downloads from the central processing cloud can bedesirable where delivery is made through non-traditional means, such asa multi-cast via satellite. Such non-point-to-point delivery approachescan provide substantial efficiencies compared to traditional internetbackhauls.

In addition, the content is evaluated for addition to the “channel” ofaggregated content stored on the local content store 110, as explainedin greater detail hereinafter.

In addition to determining whether requested content already resides onthe central storage 140, the metadata processing cluster also stores themetadata on metadata storage 145, together with various analytics, alsoas described in greater detail hereinafter.

In the event that the requested content is not on the local storage 110,the consumer's request is forwarded to the server which hosts theoriginal URL that comprises the consumer's request. The content isretrieved from the original server 155 and its associated originalcontent storage 160, and then sent to the user 100 via the cloud 120 andnetwork appliance 105. In addition, the content is evaluated for storageeither on central storage 140, local storage 110, or both, and theassociated metadata is likewise evaluated for storage on metadatastorage 145 as explained in greater detail hereinafter.

From the simplified system diagram of FIG. 1, those skilled in the artcan appreciate that a system offering very high speed throughput ofinternet content has been described, when such content has beenchannelized for storage on, in the first instance, local content storage110, and, if not there, then on central content storage 140.

Referring next to FIG. 2, an exemplary illustration of the data transferrelationships between various layers of the infrastructure of anembodiment of the present invention can be better appreciated. Inparticular, for data resident on the central cloud storage of FIG. 1,illustrated as “on net” at 200 in FIG. 2, the download speeds from thecentral cloud storage to a local data center 205 (such as the localnetwork appliance 105 in FIG. 1) can approach 10,000 Mbps depending uponimplementation, whereas traditional content downloads, for content “off”the system of the present invention as indicated at 210 in FIG. 2,typically operates at a more leisurely 200 Mbps. Continuing with theexemplary arrangement of FIG. 2, the data center 205 may operate at datarates in the range of 1000 Mbps, with a similar data rate for theassociated local transport circuit 215 and local network 220. Inaddition, allocation policies 225 and user policies 230 can beimplemented at the transport circuit and local network, respectively, topermit cost management together with selection of performance levels.Upload speeds in such an embodiment may be in the range of 10 Mbps.

Referring next to FIG. 3, an exemplary process by which an embodiment ofthe system of the present invention responds to content requests can bebetter appreciated. The process begins at 300, and at 305 a contentrequest from a client, or consumer device, is received. At step 310, therequest is evaluated to determine if the requested content ispotentially relevant to any channel, based on rules dictated by thecentral processing cluster 135 or manually assigned to a local networkappliance 105. While the embodiments shown in FIGS. 1 and 3 include acentral processing cluster, it will be appreciated that not allembodiments require such a cluster.

If the requested content is potentially relevant to a channel, therequest and associated informatics are recorded at step 315 for localchannel information, and the content request and inferred informaticsare sent, at step 320, to channel processing. In some embodiments, suchinformatics can take a number of forms depending upon the particularimplementation. For example, the informatics can comprise a combinationof all or a part of the request data transmission, together with anyinformation that has been accumulated on the local appliance regardingcustomer demographics for that appliance, as well as that appliance'slocation on the network. “Location” in this case can comprise a relativemeasure of historical bandwidth to customers and content servers, aswell as whether the network is configured with another local applianceeither upstream or downstream of the local appliance in question.

If the content was not relevant to a channel, or after completion ofstep 320, a check is made at step 325 to determine whether the requestedcontent is already maintained in the local channel cache If the contentis maintained on the local cache, a response to the client isconstructed at step 330. If the requested content is not maintained onthe local channel cache, then the response is fetched from the originalserver, as shown at step 335. Whether the response (or content) isfetched from the original server in accordance with step 335, orconstructed from the local cache in accordance with step 330, theresponse is sent to the requesting consumer device at step 340.

It will also be appreciated by those skilled in the art that, in someembodiments, it is desirable to maintain some non-channel content on thesame local storage 110 as the channel content. For example, frequentlyaccessed images such as icons, bullets, etc., need not be included inthe channelization process of the present invention, but, by beingavailable locally, can still provide accelerated delivery which resultsin a “snappy” feel to the user.

Referring next to FIGS. 4A and 4B, the method by which incoming requestscause the requested content to be aggregated into channels can be betterappreciated. The process of FIGS. 4A and 4B is, in at least someembodiments, performed on the local network appliance 105 (FIG. 1).Alternatively, in other embodiments, at least the majority of theprocess can be performed on the remote central cloud processor. Forconvenience, the remainder of the discussion of FIGS. 4A-4B will assumethat the processing occurs on the local network appliance.

With the process starting at 400, the incoming request from the consumeris read at step 405. A check is made at 410 to determine whether eitherthe address of the client requesting the content or the address of theserver is blacklisted; if so, the request is ignored as shown at 415.However, if not, user identification is fetched at 420. The useridentification is typically blurred to ensure that personal data is notretrieved or stored, while at the same time providing sufficientlocation information and a relatively unique but irreversible means toidentify groups of users for trend analysis such that the request can beevaluated for inclusion in a channel. In some embodiments, if the userhas opted out, or is blacklisted, as shown at 425, the request isignored. If the user has not opted out and is not blacklisted, then thecontent request is selectively anonymized through the use of a secureone-way hash, as shown at 430. It will be appreciated by those skilledin the art that it is, in general more desirable to perform suchblurring at the local appliance, rather than the central cloud, tobetter protect user information against improper, or at leastundesirable, disclosure.

At this point, several things can occur, substantially although notnecessarily in parallel, and not necessarily in any order: (1) thedestination and host information is identified from the packet andprotocol headers, HTTP as an example, shown at 435; (2) the user'sbrowser of operating system information is processed at 440; (3) contentaddressing information (i.e., URL or variant thereof) is stored at 445;and, (4), content UUID is generated at 450. While HTTP protocol iscommonly used for network transmission, other protocols are acceptable.The data mined from the request is then stored at 455. The processcontinues on FIG. 5B with a check at step 460 to determine whether theresponse (i.e., the requested content) was retrieved from an externalsource. If not, the content already exists in the local storage, and sothe process advances to step 465, where the stored content istransmitted to the central service and the process finishes.

If, on the other hand, the response was from an external source,indicating that the requested content did not exist on the storage ofthe present invention, the response received from the external source isread at 470, at which point two events occur: (1) the content metricsare processed at 475; and, (2) server identifiable information isprocessed from the protocol headers associated with the response,indicated at step 480. That mined metadata representing the content ofthe response is then stored at step 485. A check is then made at step490 to determine whether the response data can be retrieved through anyother process. If the data cannot be retrieved from another process,this indicates that the content is exclusively accessible by the processby which it was retrieved the first time. In such instances, the contentis stored at step 495 and later transmitted to the central processingcloud 125, since, by definition, that content would not otherwise beavailable. At that point the requested content is transmitted to theconsumer who requested it, as shown at step 465, although it will beappreciated that the transmission to the consumer need not wait for thetransmission to the central cloud to complete.

The check done at step 490 reflects situations where data is subject togeographical or other limitations, typically imposed by the source ofthe data. For example, various servers operated by large companiesfrequently redirect a request to localized servers, which prevents aconsumer from accessing data maintained on a remote server even thoughthat data is what is desired by that particular user. Likewise, otherwebsite operators time-stamp their data to cause it to be released at acertain time, and impose restrictions which prevent the data from beingaccessed from different time zones in a way that would get around suchrelease restrictions. While the time stamp restriction can affect whenthe data is released to consumers, if the objective is to provide theuser a rapid response, it can be desirable to cache the time stampedcontent ahead of the release time, so that it is immediately availableto consumers, with the rapid throughput of the present invention, assoon as the time lock associated with the time stamp is released.However, if the data is available from other sources it is generallymore efficient for the central processing cloud 125 to fetch the contentdirectly from the original content server 155 and no burden is placed onthe local network appliance 105 to transmit the data along with themetadata.

Referring next to FIG. 5, the process begins at step 500, and theincoming submission is read at step 505. At step 510, the existing listof channels (i.e., content that has previously been aggregated as likelyto be requested by a particular group of users within a cohesive edgenetwork 115) is read. Likewise, as shown at step 515, demographicinformation associated with the network appliance 105 which hasgenerated the current request is reviewed. That demographic informationis historical data, developed based on prior requests that have beenfiltered by, for example, the process of FIG. 5, and then associated asmetadata back to the remote network appliance that generated the currentrequest. The metadata thus evolves as sufficient numbers of additionalrequests for content are processed. The metadata creates a basis fordeveloping figures of merit regarding relevancy of a particular requestto existing channels, which can be further refined by the remainingsteps of the process shown in FIG. 5.

From step 510, there will be a list of channels of aggregated contentwith which to compare the newly-received request for data, to determinewhether to include the newly-requested content in a particular channel.In an embodiment, each of the channels can be processed sequentially, inaccordance with the process shown beginning at step 520, which simplychecks to see if there are more channels to compare to the incomingcontent request. If there are more channels to process, as there will beon the first iteration, the next channel is fetched at step 525.

At this point, a plurality of filtering criteria are applied as shown at530A-n, and it will be appreciated by those skilled in the art that thelist illustrated in FIG. 5 is not exhaustive. Among the criteria whichcan be used to ascertain relevance to a particular channel ofpreviously-aggregated content can be: geographical relevance, which isrelatively weak in and of itself, but can be useful for trend anddistribution analysis; socio-economic relevance, also a relatively weakindicator of relevancy but which can evolve over time. For example, if alarge number of requests are received that are determined to beassociated with high end retail, over time the channel content will tendto adjust the criteria of the particular channel to reflect the users'choices. The socio-economic data can be derived from various steps inthe process of FIG. 5A-B, including the data developed in steps 440 and445, among others.

At step 530C, ISP vertical relevance is examined, which can be amoderately strong indicator of relevancy of the newly requested contentto that already aggregated in a particular channel. Examples couldinclude a newly received request from a five-star hotel's network, wherethe channel already includes content that is often requested on otherfive star hotels' networks, or the newly-received request originatesfrom a particular student housing facility, where requests relevant toother student housing facilities are already stored in the channel.

Media type, shown at 530D, can be a strong indicator of relevancy. Forexample, web-video forms tends to correlate to other web-videos.However, the factor can evolve as in, for example, food related webvideos may, over time establish better coherence with food blogs thansimply other “web videos.”

Content metrics, shown at 530E, can also provide some relevance, as wellas providing some sense of developing trends: for example, a trend thatweb video sizes are increasing can allow better planning for logisticsand infrastructure.

Content publisher, shown in 530F, can be a strong indicator ofrelevance, because publishers tend to be single audience. For example,if a particular server gets substantial volumes of request forHuffington Post, adding all Huffington Post content probably ensureshigh cache hits, even though other criteria may not be met.

User System relevance 530G and Server system relevance 530H arerelatively weak indicators of relevance, and mostly monitor trends intechnology adoption, whether from the user side or the publisher side.As noted above, other criteria can also be used. Likewise, the criteria530A-n are each independent, and the particular arrangement shown inFIG. 5, where one group is shown ahead of the other, is simply forconvenience and is not intended to be limiting.

Once the results of the various filters are obtained, a weightedrelevancy is calculated, as shown at step 535. The particular weightingcan be determined in any suitable manner, and can vary with theparticular implementation. Then, at 540, a check is made as to whetherthe newly requested submission is relevant to the channel beingexamined. Thresholding or other criteria can be used to determinewhether the result of the weighting step should be a basis for addingthe new content to a channel, or not. If not, the process loops back tothe next channel. If the new content is considered relevant to achannel, the submission metadata is blended with the pre-existingchannel metadata at step 545, to permit the channel metadata to evolveto better reflect the user's choices, and thus increase the likelihoodthat the cache (the local content store) will already have the content auser requests by the time he requests it. The blending process typicallyuses a “damped spring” algorithm, with iterative relaxing or increasingof the factors for the various filters, until the general “error” in thechannel is minimized; or, said in a different way, until the cache hitrate approaches 100 percent.

In addition, at step 550, the channel metadata is blended back to theremote appliance metadata, to enhance the accuracy of associatingchannels to remote appliances, and supplying those channels with newcontent as that content is discovered. To facilitate the blending whichoccurs in steps 545 and 550, the unique ID assigned to the newlyrequested (and now evaluated) content is associated with the channel towhich it is deemed relevant. The modified channel metadata is thenstored at step 560, and the process loops back to step 520, to evaluatethe content with respect to the next channel in the list. It will beappreciated by those skilled in the art that a given item of content canbe relevant to multiple channels, which can be thought of as “overlap”among the channels. In such instances, it is generally preferred tostore that content on the local storage only once. In a feature of atleast some embodiments of the invention, if, for example, “channel C” isa subset of the combination of “channel A” and “channel B”, then channelC will not transmit as an independent stream if channels A and B aretransmitting.

In the event that no channel is deemed relevant to a particular piece ofnewly requested content, as determined by a check at step 565, then anew channel is created at step 570 and stored at step 575, and theprocess finishes at step 580.

Referring next to FIG. 6, a more robust version of the network diagramof FIG. 1 is shown, where elements shown with like reference numeralsoperate in the same way. Thus, a plurality of cohesive edge networks 115is shown, distributed geographically, with many more content servers155. In addition, some of the cohesive edge networks are larger thanothers, as shown at 600 and can be connected to the internet through aregional network appliance 605 and node 610. Such a regional applianceoperates to provide the local appliances 105 an augmented, “virtuallylocal” content store. In operation, content that can be shared amongmultiple local appliances can be stored on the regional networkappliance while maintaining the desired bandwidth since the local andregional appliances are, in at least some embodiments, maintained in ageographically cohesive network which provides the substantiallyinstantaneous availability that is an aspect of several embodiments ofthe present invention. FIG. 6 further illustrates that, in someembodiments of the invention, it is possible for otherwise identicalsystems to operate efficiently in a hierarchical capacity. This permitsa regional appliance to be tuned to (or store) channel content commonamong a plurality of local appliances, while permitting the localappliances to be tuned to content which is different from that stored onother local appliances, all with the result of better content coverage.It will also be appreciated from FIG. 6 that, in some instances,original content from servers 155 can be connected to the internetthrough one or more content delivery nodes 615.

Referring next to FIG. 7, the “life cycle” of the content, as managed byan embodiment of the present invention, can be better appreciated. InFIG. 7, each parallelogram represents as “state” of the content, whileeach rectangle represents a device that either acts on the state tochange the content, or acts on the state to produce content. For theembodiment of FIG. 7, two sources of content exist: local appliances 700(including their associated content storage, as shown at 110 in FIG. 1)and original content servers 705, such as third party websites. Theremote appliances produce metadata 710 describing the content requestedby a consumer (not shown), for example by the process shown in FIGS.4A-4B, and that metadata is transmitted to the central processing cloud715. In addition, if the requested content is only available to thatlocal appliance, the local appliance also forwards the content itself,shown at 720, to the central processing cloud 715.

Original content servers 705 add content to the system either byservicing requests sent from the central processing cloud 715, or bypublishing content directly to the system. The first situation occurswhen the central processing cloud analyzes the incoming metadata (atmetadata receiving and processing block 725) and determines that itshould fetch content directly from the original content server 705. Thisoccurs when the local appliance has determined that the requestedcontent is not available solely through that local appliance, and so isthe complement to step 490 (FIG. 4B), which can, in most embodiments,only be performed on the local appliance. The requested content 730 isthus fetched from the original content servers 705 and operated on bymetadata receiving block 725, where the content is associated with achannel as shown by state 735.

The second situation occurs when content publishers push their contentto the system in anticipation of that content becoming publiclyaccessible and in demand. In this instance the published content 740 mayhave little or no metadata associated with it as yet, but can still beserved with the channels, and thus pre-seed the local appliances.Content thus added to the system is also processed by blocks 725 and735, as discussed in connection with FIGS. 1, at 130 and 135, andassociated with an existing or new channel as discussed in connectionwith FIG. 5. The content thus associated with a channel can then betransmitted for content channel storage and distribution, as shown at745, as discussed in connection with FIG. 1, and particularly elements140, 145 and 150 thereof. This permits long term aggregation andeventual channel publication as a content channel, shown at 750.

A content channel is the transformed and aggregated content that isdestined for delivery to a local appliance, in order to achieve a keyaspect of some embodiments of the invention, which is to have thecontent that consumers want locally available before they request it.The distribution method by which that channel of content is delivered tothe local appliance can be any suitable method known in the art, and is,most commonly, the traditional internet infrastructure of point-to-pointdistribution, shown at 755. Alternatively, multicasting techniques suchas satellite multicasting can also be used, which require appropriatetransmitters and receivers such as shown at 760 and 765, respectively.

Having fully described a preferred embodiment of the invention andvarious alternatives, those skilled in the art will recognize, given theteachings herein, that numerous alternatives and equivalents exist whichdo not depart from the invention. It is therefore intended that theinvention not be limited by the foregoing description, but only by theappended claims.

We claim:
 1. A method for distributing channelized content to aplurality of cohesive local networks by: evaluating, in a computer, thecontent request based on the geographical location of the request,evaluating, in a computer, the content request based on thesocio-economic relevance of the request, developing groups of contentbased on the results of the evaluating steps to anticipate futurerequests from the same geographical location, and delivering through acomputer network the channelized content to a correct local or regionalappliances based on the geographical and socio-economic relevancy.